Detection of flood disaster system based on IoT, big data and convolutional deep neural network

Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as human-instigated disasters, bring about infrastructural damages, distresses, revenue losses, injuries in addition to huge death roll. Researchers around the globe are trying to find a un...

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Vydáno v:Computer communications Ročník 150; s. 150 - 157
Hlavní autoři: Anbarasan, M., Muthu, BalaAnand, Sivaparthipan, C.B., Sundarasekar, Revathi, Kadry, Seifedine, Krishnamoorthy, Sujatha, Samuel R., Dinesh Jackson, Dasel, A. Antony
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.01.2020
Témata:
Convolutional deep neural network (CDNN)
Hadoop distributed file system (HDFS)
Missing value imputation
Normalization
Rule generation
Missing value imputation
Hadoop distributed file system (HDFS)
Normalization
Convolutional deep neural network (CDNN)
Rule generation
ISSN:0140-3664, 1873-703X
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Shrnutí:Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as human-instigated disasters, bring about infrastructural damages, distresses, revenue losses, injuries in addition to huge death roll. Researchers around the globe are trying to find a unique solution to gather, store and analyse Big Data (BD) in order to predict results related to flood based prediction system. This paper has proposed the ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep neural network (CDNN) to overcome such difficulties. First, the input data is taken from the flood BD. Next, the repeated data are reduced by using HDFS map-reduce (). After removal of repeated data, the data are pre-processed using missing value imputation and normalization function. Then, centred on the pre-processed data, the rule is generated by using a combination of attributes method. At the last stage, the generated rules are provided as the input to the CDNN classifier which classifies them as a) chances for the occurrence of flood and b) no chances for the occurrence of a flood. The outcomes obtained from the proposed CDNN method is compared parameters like Sensitivity, Specificity, Accuracy, Precision, Recall and F-score. Moreover, when the outcomes is compared other existing algorithms like Artificial Neural Network (ANN) & Deep Learning Neural Network (DNN), the proposed system gives is very accurate result than other methods.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2019.11.022